AI-assisted surveys in 2025 made by Wu Block revealed a clear pattern in the most pressing cryptocurrency questions for users across different platforms and regionsAI-assisted surveys in 2025 made by Wu Block revealed a clear pattern in the most pressing cryptocurrency questions for users across different platforms and regions

What cryptocurrency questions dominated 2025 for retail users and AI models

cryptocurrency questions

AI-assisted surveys in 2025 made by Wu Block revealed a clear pattern in the most pressing cryptocurrency questions for users across different platforms and regions.

How the AI survey on user concerns was designed

On Dec 23, 2025, researchers posed a single meta-question to several large AI models: In 2025, what were the three most frequently asked questions by users about crypto?.

This approach aimed to surface long-term discussion patterns rather than short-lived headlines.

However, to avoid homogenized, search-driven answers, the team added a strict constraint: “Please do not conduct real-time searches or cite specific articles. Answer only based on your understanding of long-term discussion patterns.” This instruction forced models such as ChatGPT and Gemini 3 to rely on internal representations instead of live data.

ChatGPT: cycle anxiety and alpha hunting

ChatGPT framed its top concerns from a trader-style perspective, highlighting three linked anxieties. First, users asked: “How much further can this bull market run? Is Bitcoin already approaching its peak?” That question reflects the central role of timing in crypto market cycles.

Moreover, the second major query focused on returns: “Does real Alpha still exist in this cycle, and how can ordinary users still make money?” A third theme emerged around structural design risks: “Do current popular projects or sectors have long-term viability, or are there structural forms of extraction?” Together, these issues show users moving from simple curiosity to more sophisticated questions on sustainability.

Grok and Perplexity: narratives, price, and scams

X Grok aligned more closely with social media narratives. Its first key question asked: “After Bitcoin’s halving, is the market in a bull phase or a bear phase?” The second centered on sector selection: “Which crypto assets or sectors are most worth investing in during 2025?”

However, a third question from Grok targeted institutions and new products: “How are spot Ethereum ETFs performing, and what is the status of institutional capital inflows?” That emphasis reflects how ETF flows and halving events shaped much of the 2025 commentary.

Perplexity AI produced answers resembling search summaries. It highlighted: “How might Bitcoin9s price evolve in 2025?” and “How can one identify and avoid cryptocurrency-related scams?” A third concern was: “How will ETFs and regulatory developments affect the crypto market?” This mix of bitcoin price outlook, fraud risk, and regulation maps directly onto information-seeking user behavior.

Claude and Gemini: timing, credibility, and technology paths

Claude, another advanced model, answered with a more cautious tone. Its users asked: “Is now the right time to buy or sell Bitcoin or Ethereum?” and “Is a particular crypto project a scam, and can it be trusted?” That said, Claude9s third core query was more educational: “How should new users start investing in crypto while managing risk?”

Gemini 3 emphasized industry structure and technology pathways. Its first major question was: “Which crypto projects have real-world application value (RWA)?” A second addressed policy: “How will current and future regulatory policies affect personal asset security?”

Moreover, Gemini highlighted a strategic endgame debate: “Among Ethereum L2s, Solana, and AI + Crypto, which technological path is most likely to emerge as the ultimate winner?” In another set of answers, Gemini returned to cycle positioning, sector potential in 2025, and how regulatory changes shape asset security and investment decisions.

Why different models surface different user priorities

The divergence in top concerns across models reflects both positioning and contextual focus. ChatGPT organizes its answers like a trader9s framework, moving from cycle limits to Alpha and then to possible extraction in popular sectors. In contrast, Grok tracks social media hotspots, centering on halving narratives, bull-versus-bear debates, and ETF capital flows.

Perplexity, by comparison, mirrors search-engine behavior. It highlights price trajectories, scam avoidance, and ETF plus regulatory developments. Claude appears more conservative, focusing on trade timing, project credibility, and risk control for newcomers who want to know how to invest crypto with fewer pitfalls.

Meanwhile, Gemini looks further down the industry stack. It stresses real-world assets, regulatory impact on personal security, and the long-term endgame choice between Ethereum L2s, Solana, and AI-driven crypto infrastructure. Overall, Chinese-language models tend to focus more intensely on cycles, halving events, and regulation, three of the most deterministic variables in crypto.

Model capability and the sharpness of answers

Another secondary but important factor is model capability. More capable systems are generally better at turning a broad topic into a concrete, structured response. They break down user concerns into clearly defined sub-questions and provide differentiated angles instead of generic templates.

However, less capable models often revert to greatest-common-denominator themes such as price, regulation, and scams. These recurring motifs are very close to widely searched common questions about cryptocurrency, but they tend to blur into each other. As a result, their answers look more similar and deliver less incremental insight across different queries.

This capability gap may not be the core driver of variation, yet in broad, open-ended prompts, it does amplify the perception of homogenization. In other words, differences in how sharply a model can structure answers make some outputs appear more nuanced than others, even when they touch on the same headline topics.

Three core themes behind 2025 user concerns

When viewed together, the survey results converge on three overarching themes: cycle positioning, profit pathways, and risk boundaries. A defining feature of the crypto market is high volatility combined with narrative-driven dynamics. That combination means correctly judging whether the market is in a bull or bear phase almost dictates every subsequent decision.

Moreover, as the market matures and becomes more crowded, users quickly shift from “Is there an opportunity?” to “Where is the opportunity, and can I still capture it?” Alpha, sector choice, and institutional capital flows via ETFs naturally become best crypto sectors type concerns appearing in high-frequency user queries.

At the same time, worries about scams, project credibility, asset security, and regulatory compliance expose the long-term reality of operating in a high-return, high-uncertainty environment. This is where many cryptocurrency questions coalesce around risk: users chase upside while fearing hidden threats, leading to a typical pattern of behavior.

That pattern can be summarized as: first, judge the trend; second, identify opportunities; and finally, control risk. Across models from ChatGPT to Gemini, and across languages from English to Chinese, most user discussions in 2025 followed that sequence, even when phrased as very different cryptocurrency debate questions or casual conversation prompts.

In summary, the AI survey shows that beneath diverse wording and platform biases, user concerns in 2025 clustered around where we are in the cycle, how to find sustainable profits, and how to define the boundaries of acceptable risk.

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